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Joint 7th International Conference on Soft Computing and Intelligent Systems (SCIS) and 15th International Symposium on Advanced Intelligent Systems (ISIS)

作者： Yamazaki, Keisuke Tokyo Inst Technol Dept Computat Intelligence & Syst Sci Midori Ku G5-194259 Nagatsuta Yokohama Kanagawa Japan

ISBN: (纸本)9781479959556

In a probabilistic approach to cluster analysis, parametric models, such as a mixture of Gaussian distributions, are often used. Since the parameter is unknown, it is necessary to estimate both the parameter and the labels of the clusters. Recently, the statistical properties of Bayesian clustering have been studied. The theoretical accuracy has been analyzed, and it has been found to be better than the maximum-likelihood method, which is based on the expectation-maximization algorithm. However, the effect of a prior distribution on the clustering result remains unknown. In the present paper, we theoretically and experimentally investigate the behavior of the optimal hyperparameter, which is the parameter of the prior distribution, and we propose an evaluation method for the clustering result, based on the prior optimization.

关键词： Bayes methods Gaussian distribution expectation-maximisation algorithm optimisation pattern clustering Bayesian clustering cluster analysis expectation-maximization algorithm maximum-likelihood method optimal hyperparameter parameter estimation parametric model prior distribution prior optimization probabilistic approach statistical property Accuracy Maximum likelihood estimation Optimization Probabilistic logic Shape

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Measurements and Analysis of a 4x4 Vehicle-to-Ground MIMO Channel Using SAGE

Measurements and Analysis of a 4x4 Vehicle-to-Ground MIMO Ch...

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International Symposium on Wireless Personal Multimedia Communications (WPMC)

作者： Yang Jiaqi Wu Muqing Zhao Ruojun Wang Qian Beijing Univ Posts & Telecommun Beijing Key Lab Network Syst Architecture & Conve Beijing 100876 Peoples R China

ISBN: (纸本)9789860334074

The widely used broadband wireless technology enables a novel application of providing high-quality communications between vehicles and traffic control centers, which is collectively referred to as vehicle-to-ground communications. Channel modeling is in rapidly increasing demand to design and optimize the vehicle-to-ground communication systems. And practical channel models are only available when accurate estimations of the channel parameters are feasible. By adopting quadrature encoding and spectrum spreading, a novel and low-cost measurement method is proposed in this paper. The method can be implemented to achieve channel matrix accurately and furthermore determinate the parameters of a MIMO channel by using the space-alternating generalized expectation-maximization (SAGE) algorithm. Then the method was carried out in a measurement of a vehicle-mounted 4x4 MIMO system. Both large-scale and small-scale fading are investigated and analyzed. A path loss (PL) model and characteristics of channel parameters including PDP, K-factor, angle of arrival (AOA), angle of departure (AOD) and spatial correlation matrixes for V2G channel modeling are given based on the estimations. These results offer a valuable reference for future studies.

关键词： MIMO communication expectation-maximisation algorithm fading channels matrix algebra mobile radio wireless channels PDP characteristics SAGE algorithm V2G channel modeling angle of arrival angle of departure broadband wireless technology channel matrix channel parameters low-cost measurement method path loss model quadrature encoding small-scale fading space-alternating generalized expectation-maximization algorithm spatial correlation matrices traffic control centers vehicle-to-ground MIMO channel Correlation Delays MIMO Receiving antennas Transmitting antennas Vectors Wireless communication angles of departure SAGE algorithm expectation-maximisation algorithm channel matrix multiple input multiple output systems Angle of arrival Receiving antennas transmitting antennas MIMO communication wireless channels Mobile radios matrix algebra CARRIER SYSTEM Fading channels Wireless Technology

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Localization of Multiple Sources from a Binaural Head in a Known Noisy Environment

Localization of Multiple Sources from a Binaural Head in a K...

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Alban Portello Gabriel Bustamante Patrick Danes Alexis Mifsud CNRS LAAS 7 avenue du colonel Roche F-31400 Toulouse France

ISBN: (纸本)9781479969357

This paper presents a strategy to the localization of multiple sound sources from a static binaural head. The sources are supposed W-Disjoint Orthogonal and their number is assumed known. Their most likely azimuths are computed by means of the expectation-Maximization algorithm. Application of the method on simulated data is reported, as well as some evaluations of its HARK implementation on experimental data. Two important properties are observed: scattering effects can be coped with, thanks to the required prior knowledge of the (room-independent) head interaural transfer function;the environment noise statistics are handled separately.

关键词： Noisy environment Head simulated data expectation-maximisation algorithm acoustic radiators

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Design of a Cosmic-ray Muon Tomography System for Dry Storage Cask Imaging

Design of a Cosmic-ray Muon Tomography System for Dry Storag...

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IEEE Nuclear Science Symposium

作者： Can Liao Haori Yang Department of Nuclear Engineering and Radiation Health Physics Oregon State University

ISBN: (纸本)9781479960989

The overarching goal of this project is to develop an imaging system to monitor the content of a dry storage cask (DSC) with cosmic ray muons. The imaging technique utilizing multiple Coulomb scattering was demonstrated to be a good non-destructive assay method for high-Z materials, such as used nuclear fuel assemblies in DSCs. In this paper, we report our simulation work on the 2D and 3D muon tomography of a DSC. The Point of Closest Approach (POCA) and Maximum-Likelihood expectation-Maximization (ML-EM) algorithms are used for image reconstruction. The reconstructed image showed good spatial resolution and good material discrimination ability, even though the detector has only 1cm by 1cm spatial resolution. The prototype design uses EJ-200 plastic scintillator as the detector for muon detection. Saint-Gobain BCF-92 wavelength shifting (WLS) optical fiber is used for the light transfer providing multi-channel position information. The optical fibers are coupled to two Hamamatsu H8500C MAPMTs. The PMT signals could then be measured with multi-channel QDCs. SiPM arrays are also being evaluated for readout. This prototype system is currently under construction. Preliminary tests results are shown in this paper. Full experimental results will be presented and discussed in the future.

关键词： FUEL ASSEMBLIES Imaging cosmic rays Casks detector COULOMB SCATTERING Differential scanning calorimetry MUONS Imaging systems Spatial resolution image reconstruction Optical Fibers expectation-maximisation algorithm

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A Signal Processing Technique for Compensating Random Drift of MEMS Gyros

A Signal Processing Technique for Compensating Random Drift ...

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2014中国制导、导航与控制学术会议

作者： Jieyu Liu Qiang Shen Weiwei Qin Department of Automatic Control Xi''an Research Institute of High-techXi''anShannxi Province710025China

ISBN: (纸本)9781479946983

In this paper,we present a prediction and compensation method for Micro-Electro-Mechanical System (MEMS) gyroscope random drift,which is based on relevance vector *** relevance vector machine (RVM) model is established based on the feature of MEMS gyroscope random drift and the parameters are trained by the expectation Maximization (EM) *** phase space reconstruction,the time sequence of random drift is accessed in the *** final experimental results indicate that our proposed methodology can achieve both the least complexity of structure and goodness of fit to data,and also can predict the gyroscope random drift ***,by compensating random drift using the predicting result,the precision of gyroscopes application could be improved well.

关键词： compensation expectation-maximisation algorithm gyroscopes learning (artificial intelligence) microsensors signal processing EM algorithm MEMS gyroscope RVM model compensation method expectation maximization algorithm microelectromechanical systems phase space reconstruction prediction method random drift compensation relevance vector machine signal processing technique Bayes methods Gyroscopes Kernel Micromechanical devices Prediction algorithms Support vector machines Vectors

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Gaussian-Laplacian Mixture Model for Electricity Market

Gaussian-Laplacian Mixture Model for Electricity Market

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IEEE Annual Conference on Decision and Control

作者： Saahil Shenoy Dimitry Gorinevsky Department of Physics Stanford University Stanford CA 94305 USA Department of Electrical Engineering Stanford University Stanford CA 94305 USA

ISBN: (纸本)9781467360890

This paper develops a statistical modeling and estimation approach combining robust regression and long tail estimation. The approach can be considered as a generalization of Huber regression in robust statistics. A mixture of asymmetric Laplace and Gaussian distributions is estimated using an EM algorithm. The approach estimates the regression model, distribution body, distribution tails, and boundaries between the body and the tails. As an application example, the model is estimated for historical power load data from an electrical utility. Practical usefulness of the model is illustrated by stochastic optimization of electricity order in day-ahead market. The computed optimal policy improves the cost compared to the baseline approach that relies on a normal distribution model.

关键词： power markets Gaussian distribution Mixture models decentralised control statistical models (nuclear) expectation-maximisation algorithm Electric loads ELECTRIC UTILITIES body distribution optimal strategy

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Bayesian track-before-detect algorithm with target amplitude fluctuation based on expectation-maximisation estimation

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IET Radar, Sonar and Navigation 2012年第8期6卷 719-728页

作者： Xia, S.Z. Liu, H.W. National Lab of Radar Signal Processing Xidian University 710071 Shaanxi China

Bayesian track-before-detect is an efficient approach to detect low observable targets. Before implementing Bayesian track-before-detect, one needs to exactly ascertain the target motion model and the measurement model. When the target return amplitude fluctuates, the target return amplitude of the measurement model is not known a priori. In the scenario, standard Bayesian track-before-detect algorithms such as particle filters, which assume perfect knowledge of the model parameters, cannot work well. In this study, the authors propose an expectation-maximisation (EM) algorithm for Bayesian track-before-detect with target amplitude fluctuation, in which the fluctuation models are incorporated into the likelihood function, and the average target return amplitude is estimated by the EM algorithm. The simulation results show that the average target return amplitude can be estimated by the EM algorithm, which is helpful in improving the performance of detection and tracking. Therefore it is feasible to apply the EM algorithm to Bayesian track-before-detect with target amplitude fluctuation. © 2012 The Institution of Engineering and Technology.

关键词： radar signal processing model parameter expectation-maximisation algorithm Bayesian track-before-detect algorithm likelihood function Radar equipment, systems and applications particle filtering (numerical methods) particle filter measurement model target motion model Bayes methods expectation-maximisation estimation Filtering methods in signal processing fluctuation model EM algorithm target amplitude fluctuation object detection average target return amplitude observable target detection target tracking Other topics in statistics

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Values below detection limit in compositional chemical data

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ANALYTICA CHIMICA ACTA 2013年 764卷 32-43页

作者： Palarea-Albaladejo, J. Martin-Fernandez, J. A. JCMB Biomath & Stat Scotland Edinburgh EH9 3JZ Midlothian Scotland Univ Girona Dept Comp Sci & Appl Math E-17071 Girona Spain

Samples representing part of a whole, usually called compositional data in statistics, are commonplace in analytical chemistry say chemical data in percentage, ppm, or mu g g(-1). Their distinctive feature is that there is an inherent relationship between all the analytes constituting a chemical sample as they only convey relative information. Some compositional data analysis principles and the log-ratio based methodology are outlined here in practical terms. Besides, one often finds that some analytes are not present in sufficient concentration in a sample to allow the measuring instruments to effectively detect them. These non-detects are usually labelled as "

关键词： Compositional data Detection limits expectation-maximisation algorithm Log-normal distribution Log-ratio approach Imputation methods

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Modified student's t-hidden Markov model for pattern recognition and classification

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IET SIGNAL PROCESSING 2013年第3期7卷 219-227页

作者： Zhang, Hui Wu, Qing Ming Jonathan Thanh Minh Nguyen Nanjing Univ Informat Sci & Technol Jiangsu Engn Ctr Network Monitoring Nanjing Jiangsu Peoples R China Univ Windsor Dept Elect & Comp Engn Windsor ON N9B 3P4 Canada Nanjing Univ Informat Sci & Technol Sch Comp & Software Nanjing Jiangsu Peoples R China

The Gaussian hidden Markov model has been successfully used in pattern recognition and classification applications;however, recently the Student's t-mixture model is regarded as an alternative to Gaussian mixture models, as it is more robust for outliers. The model using Student's t-mixture distribution as its hidden state is the Student's t-hidden Markov model (SHMM). The authors propose a novel Student's t-hidden Markov model, which considers the relationship among Markov states, latent components and observations by introducing a regularising scalar exponent in the component densities of the model's emission densities. Moreover, the standard SHMM can be considered as a special case of the modified SHMM with the selection of proper parameter values. Finally, the authors adopt the gradient method to estimate optimal weight parameters. Simultaneously, the expectation-maximisation algorithm is used to fit the modified SHMM. Thus, our model is simple and easy to implement. The experimental results using synthetic and real data demonstrate the improved robustness of the proposed approach.

关键词： expectation-maximisation algorithm Gaussian processes gradient methods hidden Markov models pattern classification modified student t-hidden Markov model pattern recognition applications pattern classification applications Gaussian mixture models t-mixture distribution latent components standard SHMM scalar exponent regularization component densities emission densities optimal weight parameters gradient method expectation-maximisation algorithm synthetic data real data expectation-maximisation algorithm Gaussian processes gradient methods hidden Markov models pattern classification modified student t-hidden Markov model pattern recognition applications pattern classification applications Gaussian mixture models t-mixture distribution latent components standard SHMM scalar exponent regularization component densities emission densities optimal weight parameters gradient method expectation-maximisation algorithm synthetic data real data

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Bayesian generalised likelihood ratio test-based multiple antenna spectrum sensing for cognitive radios

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IET COMMUNICATIONS 2013年第18期7卷 2151-2165页

作者： Sedighi, Saeid Taherpour, Abbas Monfared, Shaghayegh S. M. Imam Khomeini Int Univ Dept Elect Engn Qazvin Iran

In this study, the authors address the problem of multiple antenna spectrum sensing in cognitive radios by exploiting the prior information about unknown parameters. Specifically, under assumption that unknown parameters are random with the given proper distributions, the authors use a Bayesian generalised likelihood ratio test (B-GLRT) in order to derive the corresponding detectors for three different scenarios: (i) only the channel gains are unknown to the secondary user (SU), (ii) only the noise variance is unknown to the SU, (iii) both the channel gains and noise variance are unknown to the SU. For the first and third scenarios, the authors use the iterative expectation maximisation algorithm for estimation of unknown parameters and the authors derive their convergence rate. It is shown that the proposed B-GLRT detectors have low complexity and besides are optimal even under the finite number of samples. The simulation results demonstrate that the proposed B-GLRT detectors have an acceptable performance even under the finite number of samples and also outperform the related recently proposed detectors for multiple antenna spectrum sensing.

关键词： antenna arrays Bayes methods cognitive radio convergence of numerical methods expectation-maximisation algorithm radio spectrum management signal denoising signal detection statistical testing wireless channels Bayesian generalised likelihood ratio test cognitive radios multiple antenna spectrum sensing B-GLRT detectors channel gains secondary user noise variance iterative expectation maximisation convergence rate

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